GluonTSFramework Hyper Parameter Optimization Support

Description

Implement Hyper Parameter Optimisation (HPO) Support in GluonTSFramework. There are already multiple comments in the code of how to go about it:

# HPO implementation sketch:
#    > Example HPO of model: MODEL_HPM:Trainer:batch_size:64
#    > Now construct nested dict from MODEL_HPM hyperparameters
#    > Load the serialized model as a dict
#    > Update the model dict with the nested dict from the MODEL_HPMs
#      with dict.update(...)
#    > Write this new dict back to a s3 as a .json file like before

This is important to support:

• The HyperparameterTuner: https://sagemaker.readthedocs.io/en/stable/tuner.html
• and SageMaker Experiments: https://aws.amazon.com/blogs/aws/amazon-sagemaker-experiments-organize-track-and-compare-your-machine-learning-trainings/

References

• Where the https://github.com/awslabs/gluon-ts/blob/master/src/gluonts/nursery/sagemaker_sdk/estimator.py